mhtree.VFV {mclust1998} | R Documentation |
Computes a classification tree for agglomerative hierarchical clustering using a Gaussian model in which clusters have a constant shape and are of equal volume.
mhtree.VFV(data, partition, min.clusters = 1, shape, alpha = 1)
data |
matrix of observations. |
partition |
initial classification of the data. The default puts every observation in a singleton cluster. |
min.clusters |
minimum number of clusters desired. The default is to carry out agglomerative hierarchical clustering until termination, that is, until all observations belong to a single group. The default value is 1. |
shape |
positive vector of length equal to the dimension of the observations.
The values of shape should be proportional to the squares of the lengths
of the semi-major axes of the corresponding ellipsoid, and may be given in
any order. Note that shape is a required argument.
|
alpha |
The value alpha times the trace of the sample crossproduct matrix of all
the observations divided by product of the data dimensions, is used for the
purpose of initalization.
The default value is 1.
|
an object of class "mhtree"
, which consists of a classification tree with
the following attributes:
call |
a copy of the call to mhtree.VFV .
|
change |
value of the optimal change in likelihood at each stage. |
dimensions |
the data dimensions. |
initial.partition |
the partition at which agglomerative hierarchical clustering is initiated. |
The value alpha
is needed because the criterion is not defined for singleton
clusters or clusters consisting only of points that coincide.
J. D. Banfield and A. E. Raftery, Model-based Gaussian and non-Gaussian Clustering, Biometrics, 49:803-821 (1993).
C. Fraley, Algorithms for Model-based Gaussian Hierarchical Clustering, Technical Report No. 311, Department of Statistics, University of Washington (October 1996), to appear in SIAM Journal on Scientific Computing.
mhtree
, mhclass
, awe
, partuniq
data(iris) shape <- c(1,1/2,1/3) mhtree.VFV(iris[,1:3], shape = shape)